1 contributor
# autoSMART OpenAI Configuration # AI prediction engine settings [openai] # API Configuration api_key = sk-your-openai-api-key-here api_endpoint = https://api.openai.com/v1 model = gpt-4 max_tokens = 2048 temperature = 0.1 # Low temperature for consistent predictions # Request limits and retry max_requests_per_hour = 100 retry_attempts = 3 retry_delay = 5 # seconds between retries request_timeout = 60 # seconds [prediction] # Prediction parameters prediction_window_days = 30 # Predict failures within 30 days confidence_threshold = 0.7 # Minimum confidence for alerts historical_data_days = 90 # Use 90 days of historical data minimum_readings = 10 # Minimum readings before prediction # AI prompt configuration system_prompt = "You are an expert HDD failure prediction system. Analyze SMART data and provide failure probability with reasoning." include_context = true # Include disk model, age, environment include_trends = true # Include trend analysis in prompts [analysis] # Analysis frequency full_analysis_hours = 24 # Full AI analysis every 24 hours quick_check_hours = 6 # Quick check every 6 hours emergency_check_minutes = 30 # Emergency analysis for critical values # Batch processing batch_size = 10 # Analyze 10 disks per batch batch_delay = 2 # seconds between batch requests [features] # Feature engineering for AI enable_trend_analysis = true enable_anomaly_detection = true enable_correlation_analysis = true enable_environmental_factors = true # Advanced features enable_model_specific_analysis = true # Different analysis per HDD model enable_failure_clustering = true # Group similar failure patterns enable_seasonal_adjustment = true # Account for seasonal temperature changes